Unsupervised-GFANC: Unsupervised Learning Based End-to-End Delayless Generative Fixed-Filter Active Noise Control
This repository contains the code for the paper "Unsupervised Learning Based End-to-End Delayless Generative Fixed-Filter Active Noise Control," accepted by the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024). The paper is available on ArXiv and IEEE Xplore.
- Simplified Training Process: The 1D CNN in our unsupervised-GFANC method does not require initial training using labelled noise data, simplifying the training process and enhancing practicality.
- End-to-End Differentiable ANC System: Integrating the co-processor and real-time controller into the ANC system allows for using the accumulated squared error signal as the loss for training the 1D CNN.
- Improved Noise Reduction Performance: The unsupervised-GFANC method exhibits better noise reduction performance than the supervised-GFANC method by avoiding labelling errors.
- Pre-trained Model: If you don't want to retrain the 1D CNN (
M5_Network.py
), you can use the trained model available inmodels/1DCNN_SyntheticDataset_UnsupervisedLearning.pth
. Simply run theNoise_Cancellation_RealNoise_RealPath.ipynb
notebook to get the noise reduction results. - Training Dataset: The 1D CNN is trained using a synthetic noise dataset with label files
Soft_Index.csv
. The entire dataset is available here. - Applying to New Acoustic Paths: We have provided the sub control filters on synthetic acoustic paths and our measured acoustic paths. If you want to use the Unsupervised-GFANC method on new acoustic paths, just obtain the corresponding pre-trained broadband control filter and decompose it into sub control filters. The trained 1D CNN in Unsupervised-GFANC can remain unchanged. For more details, please refer to the paper.
- Deep Generative Fixed-Filter Active Noise Control
- Delayless Generative Fixed-filter Active Noise Control based on Deep Learning and Bayesian Filter
- GFANC-Kalman: Generative Fixed-Filter Active Noise Control with CNN-Kalman Filtering
- A hybrid sfanc-fxnlms algorithm for active noise control based on deep learning
- Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks
If you are interested in our works, please consider citing our papers. Thanks for your interest! Have a great day!